Are you curious about how different devices perform under AI-heavy workloads? Look no further, as Geekbench has recently launched a new cross-platform tool called Geekbench AI that measures a device’s CPU, GPU, and NPU to determine its performance in handling machine learning applications. In this blog post, we’ll delve into the details of this exciting new tool and explore how it evaluates different hardware in AI-related tasks.
A Closer Look at Geekbench AI:
Geekbench developer Primate Labs has been working diligently on this software, initially under the name Geekbench ML. The tool evaluates performance based on accuracy and speed, supporting various frameworks such as ONNX, CoreML, TensorFlow Lite, and OpenVINO. It delivers three scores – full precision, half precision, and quantized – along with an accuracy measurement to assess how close a workload’s outputs are to the truth.
Real-World Applications:
As we move towards on-device AI, the implications of Geekbench AI’s performance metrics become even more intriguing. Imagine checking the accuracy of predictive text or exploring the capabilities of a generative AI-enabled image editor. This tool opens up a whole new realm of possibilities for device performance evaluation.
Try it Yourself:
Excited to test out Geekbench AI for yourself? You can download the tool now on Windows, macOS, Linux, Android, and iOS. Dive into the world of AI performance evaluation and see how your device stacks up against the competition.
In conclusion, Geekbench AI offers a fascinating glimpse into the future of device performance evaluation in the age of AI. Stay tuned for more updates on how this tool revolutionizes the way we assess hardware capabilities in a rapidly evolving technological landscape. Happy benchmarking!